| IHME | WHO | Dif | % difference (WHO / IHME) | % difference (IHME / WHO) | ||
|---|---|---|---|---|---|---|
| Total | HIV+TB only | 211604 | 389042 | 177438.13321 | 83.85% | -45.61% |
| TB only | 1111312 | 1379440 | 268128.44955 | 24.13% | -19.44% | |
| Total TB | 1322916 | 1768482 | 445566.58275 | 33.68% | -25.19% | |
| Adults | HIV+TB only | 177567 | 348026 | 170458.90473 | 96% | -48.98% |
| TB only | 1075691 | 1210620 | 134929.12946 | 12.54% | -11.15% | |
| Total TB | 1253257 | 1558645 | 305388.03419 | 24.37% | -19.59% | |
| Children | HIV+TB only | 34037 | 41016 | 6979.22848 | 20.5% | -17.02% |
| TB only | 35621 | 168821 | 133199.32009 | 373.93% | -78.9% | |
| Total TB | 69659 | 209837 | 140178.54857 | 201.24% | -66.8% | |
| Female | HIV+TB only | 78110 | 143496 | 65386.51804 | 83.71% | -45.57% |
| TB only | 367764 | 352488 | 15276.43876 | -4.15% | 4.33% | |
| Total TB | 445874 | 495984 | 50110.07929 | 11.24% | -10.1% | |
| Male | HIV+TB only | 99457 | 204471 | 105013.90757 | 105.59% | -51.36% |
| TB only | 707927 | 858132 | 150205.56821 | 21.22% | -17.5% | |
| Total TB | 807383 | 1062603 | 255219.47578 | 31.61% | -24.02% | |
| AMR | HIV+TB only | 579 | 620 | 41.31917 | 7.14% | -6.66% |
| TB only | 2036 | 1914 | 122.37010 | -6.01% | 6.39% | |
| Total TB | 2615 | 2534 | 81.05093 | -3.1% | 3.2% | |
| EMR | HIV+TB only | 165 | 533 | 368.30203 | 223.05% | -69.04% |
| TB only | 14658 | 14572 | 85.51575 | -0.58% | 0.59% | |
| Total TB | 14823 | 15106 | 282.78629 | 1.91% | -1.87% | |
| EUR | HIV+TB only | 212 | 374 | 161.68749 | 76.15% | -43.23% |
| TB only | 2383 | 2999 | 615.93832 | 25.85% | -20.54% | |
| Total TB | 2595 | 3373 | 777.62581 | 29.97% | -23.06% | |
| SEA | HIV+TB only | 19310 | 28870 | 9560.04060 | 49.51% | -33.11% |
| TB only | 333250 | 345889 | 12639.43214 | 3.79% | -3.65% | |
| Total TB | 352560 | 374759 | 22199.47275 | 6.3% | -5.92% | |
| WPR | HIV+TB only | 2057 | 2010 | 47.13348 | -2.29% | 2.35% |
| TB only | 39055 | 28351 | 10704.20283 | -27.41% | 37.76% | |
| Total TB | 41112 | 30361 | 10751.33632 | -26.15% | 35.41% |
Table with model output for estimating likelihood or magnitude of difference in estimates by HIV, age, sex, and region.
This section is unfinished.
Rankings of highest absolute and standardized differences for IHME and WHO.
Rankings of highest absolute and standardized differences for IHME and WHO.
The below scatterplot shows the correlation between WHO (x-axis) estimates and IHME (y-axis) estimates, with each point colored by its (WHO-defined) region.
In the following four charts, Libya has been excluded as an outlier.
Linear regression to estimate effect of prevalence survey on absolute difference in cases (WHO minus IHME), adjusting for region.
95% confidence intervals
Linear regression to estimate effect of prevalence survey on adjusted standardized difference in cases, adjusting for region.
95% confidence intervals
(Unfinished)
Correlation of adjusted stand diff with a) HIV prevalence, CDR by both, CFR, MDR prevalence.
cor(df$adjusted_stand_dif, df$newrel_hivpos, use = 'complete.obs')
[1] 0.2277724
cor(df$adjusted_stand_dif, df$gb_c_cdr, use = 'complete.obs')
[1] -0.3776421
cor(df$adjusted_stand_dif, df$cdr_ihme, use = 'complete.obs')
[1] 0.01413998
cor(df$adjusted_stand_dif, df$case_fatality_rate_2014, use = 'complete.obs')
[1] -0.04647787
cor(df$adjusted_stand_dif, df$case_fatality_rate_2012_to_2014, use = 'complete.obs')
[1] -0.0384391
cor(df$adjusted_stand_dif, df$case_fatality_rate_2015, use = 'complete.obs')
[1] -0.05203254
cor(df$adjusted_stand_dif, df$case_fatality_rate_2014_new, use = 'complete.obs')
[1] -0.04351185
cor(df$adjusted_stand_dif, df$case_fatality_rate_2012_to_2014_new, use = 'complete.obs')
[1] -0.03215424
cor(df$adjusted_stand_dif, df$case_fatality_rate_2015_new, use = 'complete.obs')
[1] -0.04752176
cor(df$adjusted_stand_dif, df$case_fatality_rate_2015_adjusted, use = 'complete.obs')
[1] -0.0491367
cor(df$adjusted_stand_dif, df$p_mdr_new, use = 'complete.obs')
[1] -0.009870141
cor(df$adjusted_stand_dif, df$reported_mdr, use = 'complete.obs')
[1] -0.02881747
Does region affect likelihood of having a prevalence survey?
xt <- table(df$prevsurvey, df$who_region)
xt
AFR AMR EMR EUR SEA WPR
0 37 37 20 52 8 22
1 10 0 2 0 3 4
chisq.test(xt)
Pearson's Chi-squared test
data: xt
X-squared = 21.511, df = 5, p-value = 0.0006482
Does having a prev survey affect the adjusted stand diff?
t.test(x = df$adjusted_stand_dif[df$prevsurvey == 0],
y = df$adjusted_stand_dif[df$prevsurvey == 1])
Welch Two Sample t-test
data: df$adjusted_stand_dif[df$prevsurvey == 0] and df$adjusted_stand_dif[df$prevsurvey == 1]
t = -1.1254, df = 18.051, p-value = 0.2751
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-17.370292 5.250238
sample estimates:
mean of x mean of y
0.4643521 6.5243789